Predicting decline in left ventricular function after new-onset left bundle branch block

BACKGROUND Left bundle branch block (LBBB) has been associated with an increased risk of incident left ventricular systolic dysfunction. Identifying high-risk patients early is challenging but important for timely management. OBJECTIVES To identify predictors of significant left ventricular ejection fraction (LVEF) decline in patients with LBBB using clinical, echocardiographic, and electrocardiographic data. METHODS A retrospective cohort of 769 patients with newly diagnosed LBBB and preserved LVEF (≥50%) was analyzed. Univariable and multivariable Cox proportional hazards regression analyses were used to identify predictors of ≥20% LVEF decline on follow-up echocardiography as the primary outcome. A risk prediction model was constructed using pooled estimates. RESULTS Over a median follow-up of 4.9 years (IQR 2.3-8.4), 242 patients (31.5%) experienced LVEF decline ≥20%. In multivariable Cox regression, two AI-derived ECG scores previously developed to detect LV systolic and diastolic dysfunction were independently associated with LVEF decline (HR=1.01, per 1% probability increase for low LVEF; p=0.005; and HR=1.25, per predicted diastolic dysfunction grade, p=0.005, respectively). The final model demonstrated modest discriminative ability with C-index of 0.615, improving to 0.65 after adjusting for baseline LVEF and further to 0.70 in a landmark analysis of 1-year follow-up. CONCLUSIONS AI-derived ECG markers of LV systolic and diastolic dysfunction independently predicted future LVEF decline in patients with new-onset LBBB. Echocardiographic parameters may also enhance risk stratification. This predictive framework could be utilized to support monitoring and early intervention strategies in patients with LBBB.